Patient and Caregiver Understanding of Prognosis After Hip Fracture
Bibliographic record
Abstract
BackgroundHip fracture (HF) is common and requires communication between patient, family, surgeons, and hospitalists. Patient and family understanding of the seriousness of HF is unclear.MethodsWe interviewed older patients (age > 65 years) hospitalized with HF at two Canadian academic hospitals, or their surrogate decision-makers (SDMs). We used qualitative methods to explore understanding of HF treatment options and prognosis. Participants estimated probability of mortality and living independently 30 days after surgery. Results were compared with estimates from the National Surgery Quality Improvement Program (NSQIP) surgical risk calculator.Results9 patients and 3 SDMs were interviewed. Mean age of 12 patients was 82.5 years (75% female). Participants were uncertain about recovery timeline and degree of functional recovery, as well as content and duration of rehabilitation. Participants’ mean estimated 30-day mortality of 6.7% did not differ significantly from estimated mortality predicted by NSQIP (7.5%; p = .88). Participants’ mean estimated probability of living independently 30 days after surgery was 90.8% (range 65–100%).ConclusionsOlder patients and SDMs lack understanding about prognosis and functional recovery even after providing informed consent for HF surgery. Clinical teams should improve communication of prognosis and recovery information to patients and surrogates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".